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HU Credits:
2
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Brain Science: Computation & Information Proc.
Semester:
Teaching Languages:
English
Campus:
E. Safra
Course/Module Coordinator:
Rachel Cohen
Coordinator Office Hours:
Teaching Staff:
Prof Yoram Burak,
Ms. Rachel Cohen
Course/Module description:
ELSC Self-Study Supplementary Math Course- Linear Algebra for Neuroscience
Course/Module aims:
Learning outcomes - On successful completion of this module, students should be able to:
to apply the basic tools from Linear Algebra.
This course is essential for participation in the more advanced mandatory courses of ELSC program.
Attendance requirements(%):
Teaching arrangement and method of instruction:
Course/Module Content:
● Vector and matrix arithmetics: elementary operations between vectors and matrices, linear combinations, linear dependence and independence, elementary matrices, invertibility, trace, similarity between matrices.
● Determinants: calculation, geometrical interpretation, multiplication rules and the effect of elementary operations.
● Systems of linear equations: general solutions of homogeneous and inhomogeneous systems, dependency on parameters, the inverse matrix.
● Finite dimensional vector spaces: Rn spaces, matrix spaces, polynom spaces, spanned sub-spaces, column and row spaces of a matrix, basis and dimension.
● Linear transformations: matrix representation of transformations, basis change transformations, representation of linear transformations in different bases, the dimension theorem for linear transformations.
● Matrix diagonalization: eigenvectors, eigenvalues, characteristic polynomial, diagonalization over real and complex spaces.
● Inner product spaces: standard inner product in Rn, orthogonality, orthonormal bases (GrahmSchmidt process), vector coordinates by orthogonal basis, Cauchy-Schwartz inequality.
Required Reading:
on Moodle
Additional Reading Material:
Grading Scheme :
Written / Oral / Practical Exam 100 %
Additional information:
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